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What is cohort analysis?

Cohort analysis groups users by a shared starting point — typically when they first signed up or when they performed a specific action — and tracks their behavior over time. It is one of the most effective ways to measure retention and understand whether your product is improving for new users.

Creating a cohort

1. Navigate to Cohorts in your analytics project.
2. Click New Cohort.
3. Choose how to define the cohort:

By first-seen date

Groups users by the week or month they were first identified in Userorbit. This is the most common cohort type and is ideal for measuring new user retention.

Select First Seen as the cohort basis and choose a granularity (weekly or monthly).

By event

Groups users by when they first performed a specific event. This is useful when you want to measure retention from a milestone rather than signup.

Select Event as the cohort basis, choose the event (for example, onboarding_completed), and set the granularity.

Configuring the return event

After defining the cohort, specify the return event — the action that indicates a user came back. Common return events include:

• login or session_started — General activity retention.
• feature_used — Feature-specific retention.
• Any event that represents meaningful engagement in your product.

Reading retention tables

The cohort view displays a retention table — a grid where:

• Rows represent cohorts (groups of users from a specific time period).
• Columns represent time periods after the cohort's starting point (Week 0, Week 1, Week 2, etc.).
• Cell values show the percentage of users from that cohort who performed the return event in that period.

How to interpret the table:

• Week 0 / Month 0 is always 100% — it represents the starting period.
• Reading across a row shows how a single cohort's engagement declines (or holds) over time.
• Reading down a column shows whether newer cohorts retain better or worse than older ones at the same age.
• Color intensity helps you spot patterns — darker cells indicate higher retention.

Comparing cohorts

Comparing cohorts is where the real insights emerge. Look for these patterns:

• Improving retention curve — If newer cohorts (lower rows) have higher percentages than older ones at the same column, your product changes are working.
• Declining retention — If newer cohorts retain worse, investigate what changed — a new user experience issue, a change in acquisition channels, or a product regression.
• Stabilization point — The column where retention stops declining significantly. This is your product's natural retention floor and a key metric to track over time.

Filtering cohorts

You can apply filters to focus your cohort analysis:

• Segment filter — Analyze retention for a specific user segment, such as pro plan users only.
• Property filter — Narrow to users with specific attributes, like users from a particular country.
• Date range — Limit the analysis to cohorts from a specific time window.

Exporting cohort data

To share cohort analysis with stakeholders or perform further analysis, export the retention table as a CSV file. Click the Export button in the top-right corner of the cohort view.

Best practices

• Use weekly cohorts for products with frequent usage and monthly cohorts for products with longer usage cycles.
• Compare cohorts before and after significant product changes to measure impact.
• Combine cohort analysis with segments to understand which user groups retain best.
• Track your stabilization point over time — it is one of the best indicators of product-market fit.